Complex interacting networks and systems: Models, analysis, and algorithms

诸如互联网、无线网络、云和社交网络之类的复杂系统和网络由大量交互实体(用户和资源)组成，这些交互实体以随机方式交互，确定它们的功能和整个系统的宏观行为。为了管理网络和系统的复杂性，我们需要了解它们的动态和紧急行为，以便为它们的稳定、高效和安全运行设计算法，并提供用户体验。今天的系统在规模上有很大的不同，基本问题不仅仅是交通问题。因此，所需的方法非常不同，超越了经典的排队模型、优化、优化和资源分配。由于系统在用户、资源和空间分布方面都很大，因此我们需要了解局部行为与全局行为的问题，以及不需要太多信息的分布式算法的设计。这项研究的目的是了解异质性以及资源的大小和人口的影响，以及如何利用这些来表征绩效。

Complex systems and networks such as the internet, wireless networks, the cloud, and social networks, are comprised of large numbers of interacting entities (users and resources) that interact in a random way that determine their functionality and the macroscopic behavior of the overall system. To manage the complexity of networks and systems we need to understand their dynamic as well as emergent behavior in order to design algorithms for their stable, efficient, and secure operation as well as for providing user experience. The systems of today are vastly different in scale and the basic issues are not simply traffic issues. The methodologies required are therefore very different that go beyond classical queueing models, optimization,  and  resource allocation. Since the systems are large in terms of users, resources, and are spatially distributed we need to understand the issues of local vs. global behavior and the design of distributed algorithms that do not require too much information. The research will aim at understanding the impact of heterogeneity and the size and population of resources and how these can be used to characterize the performance.